Characteristics of criss-cross roads, non-uniform vehicle distribution and the like objectively exist in city roads, so that vehicle positions are easily changed to cause the problem of data transmission distortion of an internet-of-vehicles network layer, and the problem becomes a bottleneck hindering the development of internet-of-vehicles application services. An existing vehicle position prediction model is trained by generally utilizing historical track data of vehicles, so that consideration of complex vehicle states and real-time road condition information is lacking, and relationshipsbetween complex driving environments and vehicle driving behaviors and between the complex driving environments and vehicle position changes are mined insufficiently. For the problem, a deep learning-based method for building a position prediction model by considering vehicle driving influence factors in an internet-of-vehicles complex network comprehensively considers the vehicle driving influence factors such as vehicle body attributes, road information, driving environments and the like; in combination with a deep learning technology, the relationships between the vehicle driving influencefactors and the vehicle positions are mined; and the vehicle position prediction model is proposed, so that the purpose of improving vehicle position prediction accuracy is achieved, and assistance isprovided for improving the stability of route protocol design of the internet-of-vehicles network layer and effectively solving the data distortion problem.